Loading Packages

# importing packages
import pandas_profiling as pp

# importing pandas
import pandas as pd

# importing plotly
import plotly.express as px

Marketing Campaign Data

AcceptedCmp1 - 1 if customer accepted the offer in the 1st campaign, 0 otherwise

AcceptedCmp2 - 1 if customer accepted the offer in the 2nd campaign, 0 otherwise

AcceptedCmp3 - 1 if customer accepted the offer in the 3rd campaign, 0 otherwise

AcceptedCmp4 - 1 if customer accepted the offer in the 4th campaign, 0 otherwise

AcceptedCmp5 - 1 if customer accepted the offer in the 5th campaign, 0 otherwise

Response (target) - 1 if customer accepted the offer in the last campaign, 0 otherwise

Complain - 1 if customer complained in the last 2 years

DtCustomer - date of customer’s enrolment with the company

Education - customer’s level of education

Marital - customer’s marital status

Kidhome - number of small children in customer’s household

Teenhome - number of teenagers in customer’s household

Income - customer’s yearly household income

MntFishProducts - amount spent on fish products in the last 2 years

MntMeatProducts - amount spent on meat products in the last 2 years

MntFruits - amount spent on fruits products in the last 2 years

MntSweetProducts - amount spent on sweet products in the last 2 years

MntWines - amount spent on wine products in the last 2 years

MntGoldProds - amount spent on gold products in the last 2 years

NumDealsPurchases - number of purchases made with discount

NumCatalogPurchases - number of purchases made using catalogue

NumStorePurchases - number of purchases made directly in stores

NumWebPurchases - number of purchases made through company’s web site

NumWebVisitsMonth - number of visits to company’s web site in the last month

Recency - number of days since the last purchase

# Reading marketing_campaign csv file in a dataframe using pandas

df_marketing_campaign = pd.read_csv(r'marketing_campaign.csv')

# Creating a statistical profile report in html format using pandas_profiling

profile = pp.ProfileReport(df_marketing_campaign)
profile.to_file("pandas_profiling_marketing_campaign.html")

# Inspecting data by displaying the first few rows in the dataframe

df_marketing_campaign.head()

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	2012-09-04	58	635	...	7	0	0	0	0	0	0	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	2014-03-08	38	11	...	5	0	0	0	0	0	0	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	2013-08-21	26	426	...	4	0	0	0	0	0	0	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	2014-02-10	26	11	...	6	0	0	0	0	0	0	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	2014-01-19	94	173	...	5	0	0	0	0	0	0	3	11	0

5 rows × 29 columns

# Descriptive statistics summary using describe function of the dataframe

df_marketing_campaign.describe()

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	...	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
count	2240.000000	2240.000000	2216.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	...	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.000000	2240.0	2240.0	2240.000000
mean	5592.159821	1968.805804	52247.251354	0.444196	0.506250	49.109375	303.935714	26.302232	166.950000	37.525446	...	5.316518	0.072768	0.074554	0.072768	0.064286	0.013393	0.009375	3.0	11.0	0.149107
std	3246.662198	11.984069	25173.076661	0.538398	0.544538	28.962453	336.597393	39.773434	225.715373	54.628979	...	2.426645	0.259813	0.262728	0.259813	0.245316	0.114976	0.096391	0.0	0.0	0.356274
min	0.000000	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
25%	2828.250000	1959.000000	35303.000000	0.000000	0.000000	24.000000	23.750000	1.000000	16.000000	3.000000	...	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
50%	5458.500000	1970.000000	51381.500000	0.000000	0.000000	49.000000	173.500000	8.000000	67.000000	12.000000	...	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
75%	8427.750000	1977.000000	68522.000000	1.000000	1.000000	74.000000	504.250000	33.000000	232.000000	50.000000	...	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
max	11191.000000	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	...	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	3.0	11.0	1.000000

8 rows × 26 columns

# Learning about the missing values and data types in the dataframe

df_marketing_campaign.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB

# Learning about all the unique values in Education column

df_marketing_campaign['Education'].unique()

array(['Graduation', 'PhD', 'Master', 'Basic', '2n Cycle'], dtype=object)

# Learning about all the unique values in Marital_Status column

df_marketing_campaign['Marital_Status'].unique()

array(['Single', 'Together', 'Married', 'Divorced', 'Widow', 'Alone',
       'Absurd', 'YOLO'], dtype=object)

# Changing the date from string format to date format

df_marketing_campaign["Date_Customer"] = pd.to_datetime(df_marketing_campaign["Dt_Customer"])

# Calculating a new column of purchase by adding the amount spent on wines, fruits, meat, fish, gold, sweet

df_marketing_campaign["Total_Purchase"] = df_marketing_campaign["MntWines"] +  df_marketing_campaign["MntFruits"] + \
df_marketing_campaign["MntMeatProducts"] + df_marketing_campaign["MntFishProducts"] + df_marketing_campaign["MntGoldProds"] \
+ df_marketing_campaign["MntSweetProducts"]

# Checking all the columns of the modified dataframe

df_marketing_campaign.columns

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response',
       'Date_Customer', 'Total_Purchase'],
      dtype='object')

# Displaying the top rows of the modified dataframe

df_marketing_campaign.head()

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response	Date_Customer	Total_Purchase
0	5524	1957	Graduation	Single	58138.0	0	0	2012-09-04	58	635	...	0	0	0	0	0	3	11	1	2012-09-04	1617
1	2174	1954	Graduation	Single	46344.0	1	1	2014-03-08	38	11	...	0	0	0	0	0	3	11	0	2014-03-08	27
2	4141	1965	Graduation	Together	71613.0	0	0	2013-08-21	26	426	...	0	0	0	0	0	3	11	0	2013-08-21	776
3	6182	1984	Graduation	Together	26646.0	1	0	2014-02-10	26	11	...	0	0	0	0	0	3	11	0	2014-02-10	53
4	5324	1981	PhD	Married	58293.0	1	0	2014-01-19	94	173	...	0	0	0	0	0	3	11	0	2014-01-19	422

5 rows × 31 columns

# Count of users sorted by the year on which they where born

agewise_born_users = df_marketing_campaign.groupby(['Year_Birth'])['ID'].nunique()
agewise_born_users.head()

Year_Birth
1893    1
1899    1
1900    1
1940    1
1941    1
Name: ID, dtype: int64

# Scatterplot represents the response of customers w.r.t. their birth year and their education level

"""
https://plotly.com/python-api-reference/generated/plotly.express.scatter.html

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None,\
                       custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None,\ 
                       facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,\
                       animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None,\
                       color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None,\
                       color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None,\
                       marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None,\
                       trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, render_mode='auto',\
                       title=None, template=None, width=None, height=None)
"""

fig = px.scatter(df_marketing_campaign, x="Year_Birth", y="Education", color='Response')
fig.show()

# Scatterplot represents the date w.r.t. the amount spent on wines, fruits, meat, fish, gold, sweet

"""
https://plotly.com/python-api-reference/generated/plotly.express.scatter.html

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None,\
                       custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None,\ 
                       facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,\
                       animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None,\
                       color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None,\
                       color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None,\
                       marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None,\
                       trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, render_mode='auto',\
                       title=None, template=None, width=None, height=None)
"""

fig = px.scatter(df_marketing_campaign, x='Date_Customer', y=['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', \
                                                              'MntSweetProducts', 'MntGoldProds'])
fig.show()

# Scatterplot represents the date w.r.t. purchase amount of different items and their respective response

"""
https://plotly.com/python-api-reference/generated/plotly.express.scatter.html

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None,\
                       custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None,\ 
                       facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,\
                       animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None,\
                       color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None,\
                       color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None,\
                       marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None,\
                       trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, render_mode='auto',\
                       title=None, template=None, width=None, height=None)
"""

fig = px.scatter(df_marketing_campaign, x='Date_Customer', y=['MntWines','MntMeatProducts', 'MntGoldProds'] , color='Response')
fig.show()

# Scatterplot represents the date w.r.t. purchase mode counts and their respective response, education level

"""
https://plotly.com/python-api-reference/generated/plotly.express.scatter.html

plotly.express.scatter(data_frame=None, x=None, y=None, color=None, symbol=None, size=None, hover_name=None, hover_data=None,\
                       custom_data=None, text=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None,\ 
                       facet_col_spacing=None, error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,\
                       animation_frame=None, animation_group=None, category_orders=None, labels=None, orientation=None,\
                       color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, range_color=None,\
                       color_continuous_midpoint=None, symbol_sequence=None, symbol_map=None, opacity=None, size_max=None,\
                       marginal_x=None, marginal_y=None, trendline=None, trendline_options=None, trendline_color_override=None,\
                       trendline_scope='trace', log_x=False, log_y=False, range_x=None, range_y=None, render_mode='auto',\
                       title=None, template=None, width=None, height=None)
"""

fig = px.scatter(df_marketing_campaign, x='Date_Customer', y=['NumDealsPurchases', 'NumWebPurchases', 'NumCatalogPurchases',\
                                                              'NumStorePurchases', 'NumWebVisitsMonth'], color='Response',\
                 facet_col= 'Education')
fig.show()

Hypotheses

Hypothesis - People who accepted the offer in the last campaign have higher mean purchase compared to people who do not accept the offer.

# Calculating the mean purchase for both positive and negative responses

df_avg_marketing_campaign = pd.DataFrame(df_marketing_campaign.groupby(['Response']).aggregate({'Total_Purchase':'mean'}))
df_avg_marketing_campaign

	Total_Purchase
Response
0	538.929171
1	987.392216

Conclusion - Since the people who accepted the offer in the last campaign have higher mean purchase compared to people who do not accept the offer, we accept the above stated hypothesis.

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Hypothesis - Customers who accepted atleast one campaign offer will also accepted the offer in the last campaign.

# Calculating the count of users which responded positively to any of the 5 campaigns but responded negatively to the\
# current campaign

print(df_marketing_campaign.query('Response == 0  & AcceptedCmp1 == 1').aggregate({'AcceptedCmp1': 'count'}))
print("\n")
print(df_marketing_campaign.query('Response == 0  & AcceptedCmp2 == 1').aggregate({'AcceptedCmp2': 'count'}))
print("\n")
print(df_marketing_campaign.query('Response == 0  & AcceptedCmp3 == 1').aggregate({'AcceptedCmp3': 'count'}))
print("\n")
print(df_marketing_campaign.query('Response == 0  & AcceptedCmp4 == 1').aggregate({'AcceptedCmp4': 'count'}))
print("\n")
print(df_marketing_campaign.query('Response == 0  & AcceptedCmp5 == 1').aggregate({'AcceptedCmp5': 'count'}))
print("\n")

print("Accepted Either of the Campaign Order and still did not respond to the current campaign")
print(df_marketing_campaign.query('Response == 0  & (AcceptedCmp1 == 1 | AcceptedCmp2 == 1 | AcceptedCmp3 == 1 | \
AcceptedCmp4 == 1 | AcceptedCmp5 == 1)').aggregate({'Response': 'count'}))

AcceptedCmp1    65
dtype: int64


AcceptedCmp2    10
dtype: int64


AcceptedCmp3    86
dtype: int64


AcceptedCmp4    105
dtype: int64


AcceptedCmp5    71
dtype: int64


Accepted Either of the Campaign Order and still did not respond to the current campaign
Response    275
dtype: int64

Conclusion - Since there are 275 users who accepted any campaign but did not accept the offer from the current campaign, we reject the above stated hypothesis.

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